Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
Sebastian Raschka CPython 3.6.8 IPython 7.2.0 torch 1.1.0
Implementation of a method for ordinal regression by Niu et al. [1] applied to predicting age from face images in the AFAD [1] (Asian Face) dataset using a simple ResNet34 [2] convolutional network architecture.
Note that in order to reduce training time, only a subset of AFAD (AFAD-Lite) is being used.
import time
import numpy as np
import pandas as pd
import os
import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torchvision import transforms
from PIL import Image
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
!git clone https://github.com/afad-dataset/tarball-lite.git
Cloning into 'tarball-lite'... remote: Enumerating objects: 37, done. remote: Total 37 (delta 0), reused 0 (delta 0), pack-reused 37 Unpacking objects: 100% (37/37), done. Checking out files: 100% (30/30), done.
!cat tarball-lite/AFAD-Lite.tar.xz* > tarball-lite/AFAD-Lite.tar.xz
!tar xf tarball-lite/AFAD-Lite.tar.xz
rootDir = 'AFAD-Lite'
files = [os.path.relpath(os.path.join(dirpath, file), rootDir)
for (dirpath, dirnames, filenames) in os.walk(rootDir)
for file in filenames if file.endswith('.jpg')]
len(files)
59344
d = {}
d['age'] = []
d['gender'] = []
d['file'] = []
d['path'] = []
for f in files:
age, gender, fname = f.split('/')
if gender == '111':
gender = 'male'
else:
gender = 'female'
d['age'].append(age)
d['gender'].append(gender)
d['file'].append(fname)
d['path'].append(f)
df = pd.DataFrame.from_dict(d)
df.head()
age | gender | file | path | |
---|---|---|---|---|
0 | 39 | female | 474596-0.jpg | 39/112/474596-0.jpg |
1 | 39 | female | 397477-0.jpg | 39/112/397477-0.jpg |
2 | 39 | female | 576466-0.jpg | 39/112/576466-0.jpg |
3 | 39 | female | 399405-0.jpg | 39/112/399405-0.jpg |
4 | 39 | female | 410524-0.jpg | 39/112/410524-0.jpg |
df['age'].min()
'18'
df['age'] = df['age'].values.astype(int) - 18
np.random.seed(123)
msk = np.random.rand(len(df)) < 0.8
df_train = df[msk]
df_test = df[~msk]
df_train.set_index('file', inplace=True)
df_train.to_csv('training_set_lite.csv')
df_test.set_index('file', inplace=True)
df_test.to_csv('test_set_lite.csv')
num_ages = np.unique(df['age'].values).shape[0]
print(num_ages)
22
##########################
### SETTINGS
##########################
# Device
DEVICE = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
NUM_WORKERS = 8
NUM_CLASSES = 22
BATCH_SIZE = 512
NUM_EPOCHS = 150
LEARNING_RATE = 0.0005
RANDOM_SEED = 123
TRAIN_CSV_PATH = 'training_set_lite.csv'
TEST_CSV_PATH = 'test_set_lite.csv'
IMAGE_PATH = 'AFAD-Lite'
class AFADDatasetAge(Dataset):
"""Custom Dataset for loading AFAD face images"""
def __init__(self, csv_path, img_dir, transform=None):
df = pd.read_csv(csv_path, index_col=0)
self.img_dir = img_dir
self.csv_path = csv_path
self.img_paths = df['path']
self.y = df['age'].values
self.transform = transform
def __getitem__(self, index):
img = Image.open(os.path.join(self.img_dir,
self.img_paths[index]))
if self.transform is not None:
img = self.transform(img)
label = self.y[index]
levels = [1]*label + [0]*(NUM_CLASSES - 1 - label)
levels = torch.tensor(levels, dtype=torch.float32)
return img, label, levels
def __len__(self):
return self.y.shape[0]
custom_transform = transforms.Compose([transforms.Resize((128, 128)),
transforms.RandomCrop((120, 120)),
transforms.ToTensor()])
train_dataset = AFADDatasetAge(csv_path=TRAIN_CSV_PATH,
img_dir=IMAGE_PATH,
transform=custom_transform)
custom_transform2 = transforms.Compose([transforms.Resize((128, 128)),
transforms.CenterCrop((120, 120)),
transforms.ToTensor()])
test_dataset = AFADDatasetAge(csv_path=TEST_CSV_PATH,
img_dir=IMAGE_PATH,
transform=custom_transform2)
train_loader = DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS)
test_loader = DataLoader(dataset=test_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS)
##########################
# MODEL
##########################
class AlexNet(nn.Module):
def __init__(self, num_classes):
super(AlexNet, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
)
self.fc = nn.Linear(4096, (self.num_classes-1)*2)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
logits = self.fc(x)
logits = logits.view(-1, (self.num_classes-1), 2)
probas = F.softmax(logits, dim=2)[:, :, 1]
return logits, probas
###########################################
# Initialize Cost, Model, and Optimizer
###########################################
def cost_fn(logits, levels):
val = (-torch.sum((F.log_softmax(logits, dim=2)[:, :, 1]*levels
+ F.log_softmax(logits, dim=2)[:, :, 0]*(1-levels)), dim=1))
return torch.mean(val)
torch.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed(RANDOM_SEED)
model = AlexNet(NUM_CLASSES)
model.to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
def compute_mae_and_mse(model, data_loader, device):
mae, mse, num_examples = 0, 0, 0
for i, (features, targets, levels) in enumerate(data_loader):
features = features.to(device)
targets = targets.to(device)
logits, probas = model(features)
predict_levels = probas > 0.5
predicted_labels = torch.sum(predict_levels, dim=1)
num_examples += targets.size(0)
mae += torch.sum(torch.abs(predicted_labels - targets))
mse += torch.sum((predicted_labels - targets)**2)
mae = mae.float() / num_examples
mse = mse.float() / num_examples
return mae, mse
start_time = time.time()
for epoch in range(NUM_EPOCHS):
model.train()
for batch_idx, (features, targets, levels) in enumerate(train_loader):
features = features.to(DEVICE)
targets = targets
targets = targets.to(DEVICE)
levels = levels.to(DEVICE)
# FORWARD AND BACK PROP
logits, probas = model(features)
cost = cost_fn(logits, levels)
optimizer.zero_grad()
cost.backward()
# UPDATE MODEL PARAMETERS
optimizer.step()
# LOGGING
if not batch_idx % 150:
s = ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f'
% (epoch+1, NUM_EPOCHS, batch_idx,
len(train_dataset)//BATCH_SIZE, cost))
print(s)
s = 'Time elapsed: %.2f min' % ((time.time() - start_time)/60)
print(s)
Epoch: 001/150 | Batch 0000/0092 | Cost: 14.5346 Time elapsed: 0.47 min Epoch: 002/150 | Batch 0000/0092 | Cost: 10.1990 Time elapsed: 0.93 min Epoch: 003/150 | Batch 0000/0092 | Cost: 9.3695 Time elapsed: 1.41 min Epoch: 004/150 | Batch 0000/0092 | Cost: 8.7663 Time elapsed: 1.88 min Epoch: 005/150 | Batch 0000/0092 | Cost: 8.5821 Time elapsed: 2.36 min Epoch: 006/150 | Batch 0000/0092 | Cost: 8.8506 Time elapsed: 2.80 min Epoch: 007/150 | Batch 0000/0092 | Cost: 8.1522 Time elapsed: 3.25 min Epoch: 008/150 | Batch 0000/0092 | Cost: 9.3045 Time elapsed: 3.74 min Epoch: 009/150 | Batch 0000/0092 | Cost: 8.2951 Time elapsed: 4.21 min Epoch: 010/150 | Batch 0000/0092 | Cost: 8.1094 Time elapsed: 4.67 min Epoch: 011/150 | Batch 0000/0092 | Cost: 8.3870 Time elapsed: 5.15 min Epoch: 012/150 | Batch 0000/0092 | Cost: 8.1078 Time elapsed: 5.62 min Epoch: 013/150 | Batch 0000/0092 | Cost: 7.6846 Time elapsed: 6.11 min Epoch: 014/150 | Batch 0000/0092 | Cost: 7.7015 Time elapsed: 6.56 min Epoch: 015/150 | Batch 0000/0092 | Cost: 7.5693 Time elapsed: 7.02 min Epoch: 016/150 | Batch 0000/0092 | Cost: 7.9339 Time elapsed: 7.51 min Epoch: 017/150 | Batch 0000/0092 | Cost: 7.7916 Time elapsed: 7.97 min Epoch: 018/150 | Batch 0000/0092 | Cost: 7.1257 Time elapsed: 8.46 min Epoch: 019/150 | Batch 0000/0092 | Cost: 6.8873 Time elapsed: 8.89 min Epoch: 020/150 | Batch 0000/0092 | Cost: 7.1145 Time elapsed: 9.36 min Epoch: 021/150 | Batch 0000/0092 | Cost: 7.3858 Time elapsed: 9.78 min Epoch: 022/150 | Batch 0000/0092 | Cost: 7.5949 Time elapsed: 10.25 min Epoch: 023/150 | Batch 0000/0092 | Cost: 6.9696 Time elapsed: 10.68 min Epoch: 024/150 | Batch 0000/0092 | Cost: 7.1473 Time elapsed: 11.15 min Epoch: 025/150 | Batch 0000/0092 | Cost: 6.6147 Time elapsed: 11.61 min Epoch: 026/150 | Batch 0000/0092 | Cost: 6.3201 Time elapsed: 12.12 min Epoch: 027/150 | Batch 0000/0092 | Cost: 6.5789 Time elapsed: 12.53 min Epoch: 028/150 | Batch 0000/0092 | Cost: 6.4532 Time elapsed: 12.99 min Epoch: 029/150 | Batch 0000/0092 | Cost: 6.5590 Time elapsed: 13.44 min Epoch: 030/150 | Batch 0000/0092 | Cost: 6.4204 Time elapsed: 13.91 min Epoch: 031/150 | Batch 0000/0092 | Cost: 6.1485 Time elapsed: 14.37 min Epoch: 032/150 | Batch 0000/0092 | Cost: 6.6225 Time elapsed: 14.87 min Epoch: 033/150 | Batch 0000/0092 | Cost: 5.9400 Time elapsed: 15.32 min Epoch: 034/150 | Batch 0000/0092 | Cost: 6.0526 Time elapsed: 15.82 min Epoch: 035/150 | Batch 0000/0092 | Cost: 5.9100 Time elapsed: 16.28 min Epoch: 036/150 | Batch 0000/0092 | Cost: 5.8563 Time elapsed: 16.75 min Epoch: 037/150 | Batch 0000/0092 | Cost: 5.4942 Time elapsed: 17.20 min Epoch: 038/150 | Batch 0000/0092 | Cost: 5.3825 Time elapsed: 17.69 min Epoch: 039/150 | Batch 0000/0092 | Cost: 5.4557 Time elapsed: 18.15 min Epoch: 040/150 | Batch 0000/0092 | Cost: 5.4534 Time elapsed: 18.66 min Epoch: 041/150 | Batch 0000/0092 | Cost: 5.2443 Time elapsed: 19.08 min Epoch: 042/150 | Batch 0000/0092 | Cost: 5.2351 Time elapsed: 19.57 min Epoch: 043/150 | Batch 0000/0092 | Cost: 5.1354 Time elapsed: 20.02 min Epoch: 044/150 | Batch 0000/0092 | Cost: 5.1245 Time elapsed: 20.50 min Epoch: 045/150 | Batch 0000/0092 | Cost: 5.0352 Time elapsed: 20.96 min Epoch: 046/150 | Batch 0000/0092 | Cost: 4.7361 Time elapsed: 21.47 min Epoch: 047/150 | Batch 0000/0092 | Cost: 4.6973 Time elapsed: 21.93 min Epoch: 048/150 | Batch 0000/0092 | Cost: 4.6416 Time elapsed: 22.44 min Epoch: 049/150 | Batch 0000/0092 | Cost: 4.6076 Time elapsed: 22.89 min Epoch: 050/150 | Batch 0000/0092 | Cost: 4.5119 Time elapsed: 23.37 min Epoch: 051/150 | Batch 0000/0092 | Cost: 4.2692 Time elapsed: 23.82 min Epoch: 052/150 | Batch 0000/0092 | Cost: 4.2506 Time elapsed: 24.33 min Epoch: 053/150 | Batch 0000/0092 | Cost: 4.2682 Time elapsed: 24.79 min Epoch: 054/150 | Batch 0000/0092 | Cost: 4.7041 Time elapsed: 25.30 min Epoch: 055/150 | Batch 0000/0092 | Cost: 3.9781 Time elapsed: 25.75 min Epoch: 056/150 | Batch 0000/0092 | Cost: 4.4825 Time elapsed: 26.23 min Epoch: 057/150 | Batch 0000/0092 | Cost: 3.8956 Time elapsed: 26.70 min Epoch: 058/150 | Batch 0000/0092 | Cost: 3.8620 Time elapsed: 27.17 min Epoch: 059/150 | Batch 0000/0092 | Cost: 4.3340 Time elapsed: 27.64 min Epoch: 060/150 | Batch 0000/0092 | Cost: 3.6278 Time elapsed: 28.13 min Epoch: 061/150 | Batch 0000/0092 | Cost: 3.8466 Time elapsed: 28.61 min Epoch: 062/150 | Batch 0000/0092 | Cost: 4.0128 Time elapsed: 29.06 min Epoch: 063/150 | Batch 0000/0092 | Cost: 3.6341 Time elapsed: 29.54 min Epoch: 064/150 | Batch 0000/0092 | Cost: 3.7518 Time elapsed: 29.99 min Epoch: 065/150 | Batch 0000/0092 | Cost: 3.4808 Time elapsed: 30.47 min Epoch: 066/150 | Batch 0000/0092 | Cost: 3.8870 Time elapsed: 30.94 min Epoch: 067/150 | Batch 0000/0092 | Cost: 3.3818 Time elapsed: 31.42 min Epoch: 068/150 | Batch 0000/0092 | Cost: 3.2848 Time elapsed: 31.90 min Epoch: 069/150 | Batch 0000/0092 | Cost: 3.3755 Time elapsed: 32.40 min Epoch: 070/150 | Batch 0000/0092 | Cost: 3.3649 Time elapsed: 32.87 min Epoch: 071/150 | Batch 0000/0092 | Cost: 3.3467 Time elapsed: 33.35 min Epoch: 072/150 | Batch 0000/0092 | Cost: 2.9973 Time elapsed: 33.82 min Epoch: 073/150 | Batch 0000/0092 | Cost: 3.0707 Time elapsed: 34.29 min Epoch: 074/150 | Batch 0000/0092 | Cost: 3.3438 Time elapsed: 34.76 min Epoch: 075/150 | Batch 0000/0092 | Cost: 3.0139 Time elapsed: 35.23 min Epoch: 076/150 | Batch 0000/0092 | Cost: 3.0865 Time elapsed: 35.70 min Epoch: 077/150 | Batch 0000/0092 | Cost: 3.1229 Time elapsed: 36.16 min Epoch: 078/150 | Batch 0000/0092 | Cost: 2.9919 Time elapsed: 36.61 min Epoch: 079/150 | Batch 0000/0092 | Cost: 3.0086 Time elapsed: 37.09 min Epoch: 080/150 | Batch 0000/0092 | Cost: 2.8564 Time elapsed: 37.55 min Epoch: 081/150 | Batch 0000/0092 | Cost: 2.9466 Time elapsed: 38.00 min Epoch: 082/150 | Batch 0000/0092 | Cost: 2.7265 Time elapsed: 38.45 min Epoch: 083/150 | Batch 0000/0092 | Cost: 2.8810 Time elapsed: 38.91 min Epoch: 084/150 | Batch 0000/0092 | Cost: 2.7061 Time elapsed: 39.37 min Epoch: 085/150 | Batch 0000/0092 | Cost: 2.6701 Time elapsed: 39.87 min Epoch: 086/150 | Batch 0000/0092 | Cost: 2.6754 Time elapsed: 40.30 min Epoch: 087/150 | Batch 0000/0092 | Cost: 2.6844 Time elapsed: 40.79 min Epoch: 088/150 | Batch 0000/0092 | Cost: 2.6610 Time elapsed: 41.23 min Epoch: 089/150 | Batch 0000/0092 | Cost: 2.9059 Time elapsed: 41.72 min Epoch: 090/150 | Batch 0000/0092 | Cost: 2.6932 Time elapsed: 42.16 min Epoch: 091/150 | Batch 0000/0092 | Cost: 2.4559 Time elapsed: 42.64 min Epoch: 092/150 | Batch 0000/0092 | Cost: 2.6640 Time elapsed: 43.11 min Epoch: 093/150 | Batch 0000/0092 | Cost: 2.5860 Time elapsed: 43.59 min Epoch: 094/150 | Batch 0000/0092 | Cost: 2.6070 Time elapsed: 44.06 min Epoch: 095/150 | Batch 0000/0092 | Cost: 2.4515 Time elapsed: 44.52 min Epoch: 096/150 | Batch 0000/0092 | Cost: 2.5023 Time elapsed: 44.97 min Epoch: 097/150 | Batch 0000/0092 | Cost: 2.3886 Time elapsed: 45.45 min Epoch: 098/150 | Batch 0000/0092 | Cost: 2.6237 Time elapsed: 45.90 min Epoch: 099/150 | Batch 0000/0092 | Cost: 2.3156 Time elapsed: 46.37 min Epoch: 100/150 | Batch 0000/0092 | Cost: 2.2186 Time elapsed: 46.85 min Epoch: 101/150 | Batch 0000/0092 | Cost: 2.4205 Time elapsed: 47.32 min Epoch: 102/150 | Batch 0000/0092 | Cost: 2.4243 Time elapsed: 47.79 min Epoch: 103/150 | Batch 0000/0092 | Cost: 2.4262 Time elapsed: 48.23 min Epoch: 104/150 | Batch 0000/0092 | Cost: 2.4243 Time elapsed: 48.69 min Epoch: 105/150 | Batch 0000/0092 | Cost: 2.1756 Time elapsed: 49.15 min Epoch: 106/150 | Batch 0000/0092 | Cost: 2.1816 Time elapsed: 49.61 min Epoch: 107/150 | Batch 0000/0092 | Cost: 2.3446 Time elapsed: 50.07 min Epoch: 108/150 | Batch 0000/0092 | Cost: 2.2174 Time elapsed: 50.51 min Epoch: 109/150 | Batch 0000/0092 | Cost: 2.2063 Time elapsed: 50.99 min Epoch: 110/150 | Batch 0000/0092 | Cost: 2.3621 Time elapsed: 51.47 min Epoch: 111/150 | Batch 0000/0092 | Cost: 2.2048 Time elapsed: 51.93 min Epoch: 112/150 | Batch 0000/0092 | Cost: 1.9002 Time elapsed: 52.39 min Epoch: 113/150 | Batch 0000/0092 | Cost: 2.3146 Time elapsed: 52.88 min Epoch: 114/150 | Batch 0000/0092 | Cost: 2.2218 Time elapsed: 53.32 min Epoch: 115/150 | Batch 0000/0092 | Cost: 2.5772 Time elapsed: 53.79 min Epoch: 116/150 | Batch 0000/0092 | Cost: 1.9954 Time elapsed: 54.22 min Epoch: 117/150 | Batch 0000/0092 | Cost: 2.2189 Time elapsed: 54.72 min Epoch: 118/150 | Batch 0000/0092 | Cost: 2.0534 Time elapsed: 55.17 min Epoch: 119/150 | Batch 0000/0092 | Cost: 2.1909 Time elapsed: 55.67 min Epoch: 120/150 | Batch 0000/0092 | Cost: 1.9588 Time elapsed: 56.10 min Epoch: 121/150 | Batch 0000/0092 | Cost: 1.8609 Time elapsed: 56.57 min Epoch: 122/150 | Batch 0000/0092 | Cost: 2.2825 Time elapsed: 57.04 min Epoch: 123/150 | Batch 0000/0092 | Cost: 2.2175 Time elapsed: 57.55 min Epoch: 124/150 | Batch 0000/0092 | Cost: 2.0279 Time elapsed: 57.98 min Epoch: 125/150 | Batch 0000/0092 | Cost: 1.9375 Time elapsed: 58.46 min Epoch: 126/150 | Batch 0000/0092 | Cost: 2.0164 Time elapsed: 58.89 min Epoch: 127/150 | Batch 0000/0092 | Cost: 2.1515 Time elapsed: 59.39 min Epoch: 128/150 | Batch 0000/0092 | Cost: 1.9873 Time elapsed: 59.83 min Epoch: 129/150 | Batch 0000/0092 | Cost: 1.8686 Time elapsed: 60.30 min Epoch: 130/150 | Batch 0000/0092 | Cost: 1.9796 Time elapsed: 60.73 min Epoch: 131/150 | Batch 0000/0092 | Cost: 1.7672 Time elapsed: 61.23 min Epoch: 132/150 | Batch 0000/0092 | Cost: 1.9022 Time elapsed: 61.70 min Epoch: 133/150 | Batch 0000/0092 | Cost: 1.8617 Time elapsed: 62.19 min Epoch: 134/150 | Batch 0000/0092 | Cost: 1.7341 Time elapsed: 62.66 min Epoch: 135/150 | Batch 0000/0092 | Cost: 1.7973 Time elapsed: 63.17 min Epoch: 136/150 | Batch 0000/0092 | Cost: 1.7751 Time elapsed: 63.63 min Epoch: 137/150 | Batch 0000/0092 | Cost: 1.9271 Time elapsed: 64.14 min Epoch: 138/150 | Batch 0000/0092 | Cost: 1.6380 Time elapsed: 64.59 min Epoch: 139/150 | Batch 0000/0092 | Cost: 1.7169 Time elapsed: 65.07 min Epoch: 140/150 | Batch 0000/0092 | Cost: 1.8063 Time elapsed: 65.53 min Epoch: 141/150 | Batch 0000/0092 | Cost: 1.8708 Time elapsed: 66.03 min Epoch: 142/150 | Batch 0000/0092 | Cost: 1.4449 Time elapsed: 66.48 min Epoch: 143/150 | Batch 0000/0092 | Cost: 1.8047 Time elapsed: 66.95 min Epoch: 144/150 | Batch 0000/0092 | Cost: 1.9332 Time elapsed: 67.40 min Epoch: 145/150 | Batch 0000/0092 | Cost: 1.8951 Time elapsed: 67.85 min Epoch: 146/150 | Batch 0000/0092 | Cost: 1.6895 Time elapsed: 68.33 min Epoch: 147/150 | Batch 0000/0092 | Cost: 1.7324 Time elapsed: 68.77 min Epoch: 148/150 | Batch 0000/0092 | Cost: 1.6677 Time elapsed: 69.24 min Epoch: 149/150 | Batch 0000/0092 | Cost: 1.6663 Time elapsed: 69.71 min Epoch: 150/150 | Batch 0000/0092 | Cost: 1.7063 Time elapsed: 70.18 min
model.eval()
with torch.set_grad_enabled(False): # save memory during inference
train_mae, train_mse = compute_mae_and_mse(model, train_loader,
device=DEVICE)
test_mae, test_mse = compute_mae_and_mse(model, test_loader,
device=DEVICE)
s = 'MAE/RMSE: | Train: %.2f/%.2f | Test: %.2f/%.2f' % (
train_mae, torch.sqrt(train_mse), test_mae, torch.sqrt(test_mse))
print(s)
s = 'Total Training Time: %.2f min' % ((time.time() - start_time)/60)
print(s)
MAE/RMSE: | Train: 0.65/1.13 | Test: 3.91/5.40 Total Training Time: 70.77 min
%watermark -iv
numpy 1.15.4 pandas 0.23.4 torch 1.1.0 PIL.Image 5.3.0